Markov envelope as a theoretical solution of the parabolic wave equation with Markov approximation for the von Kármán type random medium is studied and approximated with the convolution of two probability density functions (pdf) of normal and gamma distributions considering the previous studies on the applications of Radiative Transfer Theory (RTT) and the analysis results of earthquake records. Through the approximation with gamma pdf, the constant shape parameter of 2 was determined regardless of the source distance ro. This finding means that the scattering process has the property of an inhomogeneous single-scattering Poisson process, unlike the previous studies, which resulted in a homogeneous multiple-scattering Poisson process. Approximated Markov envelope can be treated as the normalized mean square (MS) envelope for ground acceleration because of the flat source Fourier spectrum. Based on such characteristics, the path duration is estimated from the approximated MS envelope and compared to the empirical formula derived by Boore and Thompson. The results clearly show that the path duration increases proportionately to ro 1/2-ro 2, and the peak value of the RMS envelope is attenuated by exp (-0.0033ro), excluding the geometrical attenuation. The attenuation slope for ro≤100 km is quite similar to that of effective attenuation for shallow crustal earthquakes, and it may be difficult to distinguish the contribution of intrinsic attenuation from effective attenuation. Slowly varying dispersive delay, also called the medium effect, represented by regular pdf, governs the path duration for the source distance shorter than 100 km. Moreover, the diffraction term, also called the distance effect because of scattering, fully controls the path duration beyond the source distance of 300 km and has a steep gradient compared to the medium effect. Source distance 100-300 km is a transition range of the path duration governing effect from random medium to distance. This means that the scattering may not be the prime cause of peak attenuation and envelope broadening for the source distance of less than 200 km. Furthermore, it is also shown that normal distribution is appropriate for the probability distribution of phase difference, as asserted in the previous studies.

본 연구는 국내 냉동보관창고 보관온도에 대한 조사자료를 활용하여, 온도분포를 추정하였고 이를 미생물 위해 평가(microbial risk assessment; MRA)의 입력변수로 활용 할 수 있도록 적정 확률분포 모델을 제시하였다. 조사에 참여한 8곳의 냉동보관창고에서 측정된 공간상의 온도는 최저 -25.8oC, 최고 -10.3oC, 평균 -20.48 ± 3.08oC이었으며, - 18oC이상의 냉동창고 비율은 20.4%로 조사되었다. 공간별 온도분포는 자연대류를 이용하는 냉동창고의 경우 상단 (2.4~4 m) -22.57 ± 0.84oC, 중단(1.5~2.4 m) -22.49 ± 1.05oC, 하단(0.7~1.5 m) -22.68 ± 1.03oC, 최고온도차이는 1.78oC이 었으며, 강제대류를 이용하는 냉동창고의 온도분포는 상단 (2.4~4 m) -17.81 ± 1.47oC, 중단(1.5~2.4 m) -17.94 ± 1.44oC, 하단(0.7~1.5 m) -18.08 ± 1.42oC, 최고온도차이는 0.94oC로 조사되었다. 보관온도는 냉동창고 모든 공간에서 온도가 일정하게 유지되는 것이 아니라 편차가 존재하는 것으로 나타났다. 이상의 수집된 온도자료는 @RISK를 이용, 적합성 검정(GOF: A-D, K-S test)을 수행하여, MRA에서 활용할 수 있는 국내 냉동보관창고 온도분포에 대한 가장 적합한 확률분포모델로 Lognormal [5.9731,3.3483, shift(-26.4281)] 이 선정하였다.

Analysis of water quality distribution is very important for river water quality management. Recently, various studies have been conducted on the analysis of water quality distribution according to reduction methods of nonpoint pollutant. The objective of this study was to select the probability distributions of water quality constituents (T-N, T-P, COD, SS) according to the farming forms (control, slow release fertilizer, water depth control) during non-storm period in the paddy fields. The field monitoring was conducted monitoring site located in Baeksan-myun, Buan-gun, Jeollabuk-do, Korea during non-storm period from May to September in 2016. Our results showed that there were no differences in water quality among three different farming forms, except for SS of control and water depth control. K-S method was used to analyzed the probability distributions of T-N, T-P, COD and SS concentrations discharged from paddy fields. As a results of the fitness analysis, T-N was not suitable for the normal probability distribution in the slow release fertilizer treatment, and the log-normal probability distribution was not suitable for the T-P in control treatment. The gamma probability distribution showed that T-N and T-P in control and slow release fertilizer treatment were not suitable. The Weibull probability distribution was found to be suitable for all water quality constituents of control, slow release fertilizer, and water depth control treatments. However, our results presented some differences from previous studies. Therefore, it is necessary to analyze the characteristics of pollutants flowing out in difference periods according to various farming types. The result of this study can help to understand the water quality characteristics of the river.

This study aimed to estimate the odor emission rate from swine nursery facilities (naturally and mechanically ventilated) using probability distribution. Odor occurrence trends in the study facilities were very different; odor concentration and gas flow had a lognormal distribution. Monte Carlo simulation was used to carry out the uncertainty analysis. Odor emission rate was found to range from 18.05 OU/sec (10th percentile) to 621.88 OU/ sec (90th percentile), and odor emission rate per head ranged from 0.02 OU/sec · head (10th percentile) to 0.64 OU/ sec · head (90th percentile).

It was confirmed that the extreme value distribution model applies to probability of exceeding more than once a day monthly the facility capacities using data of daily maximum inflow rate for 7 wastewater treatment plant. The result of applying the extreme value model, A, D, E wastewater treatment plant has a problem compared to B, C, F, G wastewater treatment plant. but all the wastewater treatment plant has a problem except C, F wastewater treatment plant based 80% of facility capacity. In conclusion, if you make a standard in statistical aspects probability exceeding more than once a day monthly can be ‘exceed day is less than a few times annually’ or ‘probability of exceeding more than once a day monthly is less than what percent’.

The purpose of this study is to find suitable probability distribution function of complex distribution data like multimodal. Normal distribution is broadly used to assume probability distribution function. However, complex distribution data like multimodal are very hard to be estimated by using normal distribution function only, and there might be errors when other distribution functions including normal distribution function are used. In this study, we experimented to find fit probability distribution function in multimodal area, by using AIS(Automatic Identification System) observation data gathered in Mokpo port for a year of 2013. By using chi-squared statistic, gaussian mixture model(GMM) is the fittest model rather than other distribution functions, such as extreme value, generalized extreme value, logistic, and normal distribution. GMM was found to the fit model regard to multimodal data of maritime traffic flow distribution. Probability density function for collision probability and traffic flow distribution will be calculated much precisely in the future.

This study was to present the proper probability distribution models that based on the data for surveys of food cold storage temperatures as the input variables to the further MRA (Microbial risk assessment). The temperature was measured by directly visiting 7 food plants. The overall mean temperature for food cold storages in the survey was 2.55 ± 3.55oC, with 2.5% of above 10oC, −3.2oC and 14.9oC as a minimum and maximum. Temperature distributions by space-locations was 0.80 ± 1.69oC, 0.59 ± 1.68oC, and 0.65 ± 1.46oC as an upper (2.4~4 m), middle (1.5~2.4 m), and lower (0.7~1.5 m), respectively. Probability distributions were also created using @RISK program based on the measured temperature data. Statistical ranking was determined by the goodness of fit (GOF) to determine the proper probability distribution model. This result showed that the LogLogistic (−4.189, 5.9098, 3.2565) distribution models was found to be the most appropriate for relative MRA conduction.

본 연구에서는 신뢰성 개념을 도입하여 최근 10년간의 수원과 의정부 지역의 일반국도 아스팔트 포장구간의 수명 데이터를 기반으로 적합한 모수적 수명분포 선정과 헤저드 함수 및 생존확률을 추정하였다. 최적 수명분포형을 찾기 위해 확률지 개념을 이용하여 모수를 추정하였으며 적정 확률분포형태의 결정은 Anderson-Darling 통계값을 이용하였다. 그 결과 분석 대상 지역 포장의 수명 데이터를 가장 잘 설명하는 수명분포는 대수정규(Lognormal)분포인 것으로 분석되었다. 또한 본 연구에서 제안한 대수정규분포에 의해 추정된 생존확률함수는 실제 관측값과 차이가 거의 없음을 확인하였다. 본 연구에서 제안한 신뢰성 개념을 이용한 분석 방법은 포장관리 및 유지보수 데이터가 축적되어감에 따라 비교적 용이하게 계속 update가 가능하며 따라서 보다 정확한 포장수명에 대한 신뢰도 값에 접근해 갈 수 있는 이점이 있다.

마찰형 감쇠를 갖는 구조물은 구조물의 고유주기, 하중의 특성, 그리고 외부하중에 대한 마찰력의 상대적인 크기에 따라 강한 비선형성을 나타내므로, 구조물의 최대응답을 예측하기 매우 어렵다. 기존의 연구에서는 비선형 시스템을 등가의 선형 시스템으로 치환하거나, 구조물의 비선형 시간이력해석을 통한 응답스펙트럼 분석에 의한 간단한 확률해석에 의해 수행되었다. 지진 하중은 불확실성과 불규칙성을 갖고 있기 때문에 확률적으로 정의된다면, 지진하중을 받는 마찰형 감쇠를 갖는 구조물의 응답 역시 확률분포를 나타낼 것이다. 본 논문에서는 Kanai-Tajimi 필터를 이용해 생성된 인공지진하중에 대해 마찰형 감쇠를 갖는 구조물의 비선형 시간이력 해석이 수행되었다. 그리고 정규분포 확률밀도 함수에 선형 회귀분석을 통해 얻어진 구조물의 주기와 마찰력의 크기에 의한 변수를 업데이트 시킨 마찰형 감쇠를 갖는 구조물의 변위 응답 확률밀도함수식이 제시된다.

Being in an internet era, the rapid transmission of 3D mesh models is getting more Important and efforts toward the compression of various aspects of mesh models have been provided Even though a mesh model usually consists of coor-dinates of vertices and

Being in an internet era, the rapid transmission of 3D mesh models is getting more important and efforts toward the compression of various aspects of mesh models have been provided. Even though a mesh model usually consists of coordinates of vertices and properties such as colors and normals, topology plays the most important part in the compression of other information in the models. Despite the extensive studies on Edgebreaker, the most frequently used and rigorously evaluated topology compressor, the probability distribution of its five op-codes, C, R, E, S, and L, has never been rigorously analyzed yet. In this paper, we present probability distribution of the op-codes which is useful for both the optimization of the compression performance and a priori estimation of compressed file size.

Since transmitting various files around Internet is one of common activities in everyday life, the compression is important technical issue in these days. Shape models are also frequently transmitted and therefore its compression has also been studied. Considering the large portion of shape model can be normal vectors, a new scheme was recently presented to compress normal vectors using clustering and mixed indexing scheme. Presented in this paper is a mathematical investigation of the scheme to analyze the probability distribution of normal index distances in Normal Index array which is critical for the compression. The probability distribution is formulated so that the values can be easily calculated once the relative probabilities of C, R, E, S, and L op-codes in Edgebreaker are known. It can be shown that the distribution of index distances can be easily transformed into a few measures for the compression performance of the proposed algorithm.